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Slower light for faster telecom networks

Promising research could yield better optical data storage

3 min read

14 December 2007—A new method of temporarily storing light inside optical fibers may help speed up the optical telecommunications network of the future. Researchers at Duke University, in Durham, N.C., and the University of Rochester, in Rochester, N.Y., report in today’s issue of the journal Science that they have managed to store light pulses for several nanoseconds by converting them into sound waves. The ability to store data carried by light without first converting it into electricity could allow telecom networks to process that data more efficiently.

The scientists used a technique called stimulated Brillouin scattering to stop the light in its tracks. First, they sent light pulses encoded with data into one end of a 5-meter-long loop of optical fiber, the same sort of glass fiber that carries Internet and telephone communications around the world. Then they sent another set of pulses, called write pulses, with a slightly lower frequency than the data pulses, into the fiber in the opposite direction. ”When the pulses physically overlap in the fiber, they interfere with each other,” says Daniel Gauthier, a physics professor at Duke and one of the paper’s authors.

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The Future of Deep Learning Is Photonic

Computing with light could slash the energy needs of neural networks

10 min read

This computer rendering depicts the pattern on a photonic chip that the author and his colleagues have devised for performing neural-network calculations using light.

Alexander Sludds
DarkBlue1

Think of the many tasks to which computers are being applied that in the not-so-distant past required human intuition. Computers routinely identify objects in images, transcribe speech, translate between languages, diagnose medical conditions, play complex games, and drive cars.

The technique that has empowered these stunning developments is called deep learning, a term that refers to mathematical models known as artificial neural networks. Deep learning is a subfield of machine learning, a branch of computer science based on fitting complex models to data.

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